A semantic-based visualised wiki system (SVWkS) for lesson-learned knowledge reuse situated in product design

In the process of product design, engineers usually find it is difficult to precisely find and reuse others’ empirical knowledge resources, especially the lesson-learned knowledge, which is usually not well collected by the organisation. This study proposes a novel approach, which uses a semantic-based visualised wiki system (SVWkS) to support lesson-learned knowledge reuse. The core of visualised knowledge search framework is a semantic-based topic knowledge map. The architecture of this knowledge map creation method is designed, which has five major modules: lesson-learned items pre-processing, topic extraction, topic relation computation, topic weight computation and topic knowledge map generation modules. Then a working scenario of SVWkS is briefly introduced. We have conducted three sets of experiments to evaluate quality of visualised results-knowledge map, the effectiveness of semantic-based visualised searching mechanisms and the performance of utilising SVWkS for knowledge reuse in outfitting design of a ship-building company. The first experiment shows that knowledge maps generated by SVWkS are accepted by domain experts from the evaluation since precision and recall are high. The second experiment shows a semantic-based visualised searching mechanism supported by semantic relations is more useful than a traditional keyword search in terms of precision and recall. The third experiment shows that SVWkS-based group outperforms keyword search-based group in both learning score and satisfaction level, which are two measurements of performance of utilising SVWkS. The promising results confirm the feasibility of SVWkS in helping engineers to find needed lesson-learned knowledge and reuse-related knowledge.     

A quantitative approach for measuring process innovation: a case study in a manufacturing company

Process management and innovation arguably remain among the concepts under focus of recent researches since there is no significantly outstanding method to measure and monitor the level of innovation in the manufacturing processes over a particular time period taking the fundamental activities of manufacturing processes into account. Although there are various studies relevant to process improvement, manufacturing processes are not focused on in the literature. This paper presents a novel performance indicator, called degree of process innovation, for monitoring and measuring innovation in manufacturing processes based on the four most important components among the fundamental activities of a manufacturing system. The components are namely Average Labour Utilisation, Cumulative Bottleneck Ratio, Unit Production Time and Unit Production Cost. The idea behind this approach has flourished on the basis of an indicator proposed in the literature to measure the general organisational improvements. The scope of that indicator has been narrowed down to manufacturing processes to accurately reflect the state of the manufacturing processes. The proposed approach has been verified with a case study in manufacturing industry, where each of the four sub-indicators was calculated based on the data provided and aggregated into the degree of process innovation. The innovation degree is successfully indicated.     

A proposed process for systematic innovation

A systematic innovation process (SIP) derived from observations of business practices is proposed and exemplified. Time-wise, the SIP is a series of phases and stages which link the planned business processes from business opportunity identification to technology details to cross-industry application exploitation of newly developed technology/tools/products. Resource-wise, the SIP provides a platform to integrate heterogeneous resources and tools such as TRIZ (Theory of Inventive Problem Solving), non-TRIZ tools, and more opportunity identification and problem solving techniques for systematic innovation. Unlike brain-storming type innovation activities which are often ad-hoc and highly dependent on luck, systematic innovation regards the systematic development of innovative problem solving and/or opportunity identification. The proposed SIP is based on the authors’ observations of industry practices and has not been described elsewhere before. The framework integrates the full phases of systematic innovation processes providing a structured process to enable companies to systematically identify business opportunities and key problems, solve problems, and leverage developed tools/products/technologies for cross-industry exploitations. This SIP also allows for the integration of various tools and knowledge within the overall systematic and cyclic process to support systematic innovation.     

Investigating the role of business processes and knowledge management systems on performance: A multi-case study approach

In the ever-changing and competitive market place, organisations continuously need to improve their competitive advantage. One method to accomplish this is to form collaborative networks. Both knowledge management (KM) and KM systems play a pivotal role in the success of collaborative networks since information sharing and knowledge assets are so critical to the network. There has been a vast amount of research on KM systems but very little is known about how it affects individual and organisational performance. Drawing on the task–technology fit theory, in this study, we explore the fit or alignment between business process (task) and KM systems (technology) and its impact on KM systems utilisation based on multiple case studies. Subsequently, we investigate the impacts of both the task–technology fit and KM systems utilisation on individual and business performance. This paper contributes to the collaborative network/KM literature in several ways. First, it extends the task–technology fit theory to an important context of collaborative network/KM. Second, it replaces task with business process, which has the potential to help explain KM systems’ success on business performance. Third, the paper explores the positive impact of task–technology fit on KM system utilisation and business performance. Fourth and finally, the study provides insight into the future development of KM systems and how to better align them with managerial purposes.     

Impacts of government subsidies and environmental regulations on green process innovation: A nonlinear approach

With China's tremendous economic development following its reform and opening-up, the problem of environmental deterioration has become increasingly serious. To achieve a win-win situation between economic growth and environmental protection, enterprises are being encouraged to carry out green technology innovation, but due to the risks and uncertainties inherent in it, the government is providing research and development (R&D) subsidies while at the same time implementing environmental regulations. As the Organisation for Economic Cooperation and Development (OECD) [5] divides green technology innovation into green product innovation and green process innovation, this study focuses on the latter in order to better study its relationship with environmental regulations and government subsidies. We select panel data of 30 provinces and cities in China from 2009 to 2017 (excluding Tibet, Hong Kong, Macao, and Taiwan, because of a lack of data) and use the system GMM and threshold-effect model for empirical analysis. The results show that environmental regulations have a U-shape non-linear effect on green process innovation, while government subsidies have a positive role in promoting green process innovation, or the so-called leverage effect. Based on government subsidies, the impact of environmental regulations on green process innovation has a threshold effect, and therefore regulations and subsidies should be increased. In addition, the level of economic development has a U-shape effect of inhibition and then promotion on green process innovation. Overall, the China government should continue to develop its economy, but must not neglect the impact of environmental regulations on technological innovation at the expense of environmental damage.     

面向社交网络分析的差分隐私保护研究综述

阐述了数据的差分隐私保护概念,给出了差分隐私保护模型,从理论上描述了其噪声机制和组合性质,着重进述了差分隐私保护模型在社交网络发布数据隐私保护上的应用及发展,给出了差分隐私保护应用于度分布查询、子图计数、聚类系数计算、边权重计算等社交网络分析技术的实验结果。分析发现,研究差分隐私保护应重点考虑隐私预算和噪声机制,隐私预算决定了隐私保护强度,噪声机制决定了查询准确性;探讨差分隐私保护在社交网络领域的应用,是一个重要的研究方向。     

Reviewing the intellectual structure and evolution of the innovation systems approach: A social network analysis

The increased attention of policymakers and researchers to the concept of innovation systems in recent decades has led to an increase in studies in this field and, consequently, its dynamics. This increase in the number of studies indicates the necessity of conducting studies to delineate the intellectual structure and process of development and evolution of this field. The present study aims to present a picture of the structure, evolution, and dynamics of innovation systems through the analysis of academic social networks based on bibliometric criteria and identify emerging issues for research. Accordingly, using citation, word co-occurrence, co-citation, and bibliographic coupling analyses, we analyzed 3250 documents which had been published between 1988 and 2018 and extracted from the Web of Science database. Through a systematic review, theoretical roots and frameworks affecting this field were identified and the existing research streams were introduced. According to the analyses, historical and contemporary views were categorized into four clusters. In addition, the most important and emerging issues in this field were identified and the most influential documents, journals, and authors were introduced.     

应用层组播路由的一种改进神经网络解法

研究了使用双层递归神经网络(DRNN)模型求解应用层组播路由的问题.对原有模型的神经元矩阵及能量函数进行改变,并引入了新的线性编程神经元,解决了原模型不能求解组播路由的缺陷.与启发式组播路由算法相比,该解决方案的计算复杂度低,速度较快,而且与其它的神经网络相比,由于引入了基尔霍夫限制条件,保障了解的质量,且所使用的神经元数量少,而在解的精确度上则与其它算法相当.     

A simulation based approach for supply network control

We focus on the problem of strategic network design in the context of multi-site enterprises. We develop a simulation model for various supply network designs evaluation. The suggested model is generic in the sense that only input data modifications are needed to adapt the model to a given supply network structure, while the model's procedural logic stays unchanged. The model reproduces the dynamics of the totality of the supply chain activities. It takes into account control policies related to the inventory management and to the scheduling of production activities. It also carries out local optimisations by dynamically allocating the customers’ demands to the appropriate distribution site and the replenishment orders expressed by the distribution and production sites of the enterprise so as to minimise a global cost function. Simulation execution permits to compute various performance measures. These indicators inform the decision-maker about the profit of the enterprise and about its customer's service level. In this paper, we propose to detail the simulation model. Then, for a fictitious problem of supply network design based on two real life applications, we highlight the influence of the control policies on the performance of the studied enterprise.     

A new lot sentencing approach by variables inspection based on process yield

This study applies the concept of repetitive group sampling (RGS) to develop a new variables sampling plan for lot sentencing on the basis of process fraction nonconforming. The product acceptance determination problem is formulated as a nonlinear optimization problem where the objective function is to minimise the average sample number required for inspection, and the constraints are set by satisfying the acceptable quality level, limiting quality level, producer’s risk and consumer’s risk in the contract. The proposed lot sentencing approach’s behaviour is examined and discussed. The results indicate that the performance of the proposed variables RGS plan is better than that of a conventional variables single sampling plan in terms of the required sample size for inspection. Thus, the proposed approach can help the practitioner efficiently make a decision to determine whether the submitted lots should be accepted.     

Towards a multi-levelled social process perspective on firm innovation: integrating micro,meso and macro concepts of knowledge creation

Abstract

The aim of this paper is to suggest a framework for studying firm innovation processes in a social process perspective that integrates the micro, meso and macro levels of analysis, that is, the knowledge creation of individuals/communities, the knowledge coordination of organisations, and the spatial–institutional contextualisation of knowledge. For this purpose, we suggest using as a starting point the so-called Combinatorial Knowledge Bases approach, which allows merging concepts about micro practices and macro contexts in an effective way. The suggested framework pays attention to different phases in the entire innovation process, characterised by different (micro) learning modes, situated in varying (macro) contexts and delineated by organisational (meso-level) choices, therefore enabling an integrated approach to the studying of firm innovation. To illustrate the potentials of applying the framework we present a process-oriented in-depth empirical case study of an innovation, following it from idea generation to market introduction by means of an innovation biography method.     

A fast variable neighborhood search for p-hub median problem using social network analysis concepts

This study will contribute to the uncapacitated single allocation p-hub median problem (USApHMP) which is known as an NP-hard problem in the literature. This problem is concerned with locating of hub facilities in a network and allocating of each non-hub node to just one hub in order to minimize total transportation costs in the network. A hybrid variable neighborhood search (VNS) algorithm is proposed considering three structures of local search which are used as a combination of nested VNS and sequential VNS in the algorithm. For reduction of the dimensions in the nested part, social network analysis centrality measures for the node are used to choose elite points instead of all existing points in the local search structures. The obtained results demonstrate that this will not only retain quality of the solutions, but also reduce run time of the algorithm significantly. Three standard data sets (AP, CAB, and URAND) were used for numerical analysis. Computational results show that quality of the obtained solutions is good and able to compete with other heuristics addressed in the literature. From the viewpoint of execution time, it considerably outperforms all other algorithms. The intelligent search embedded in this algorithm makes it robust and efficient on networks with up to 400 nodes.     

宝钢益昌基于电子商务的知识管理体系结构

针对电子商务中知识管理的需求,以上海宝钢益昌薄板有限公司为对象,在分析宝钢益昌实施知识管理现状的基础上,设计了宝钢益昌电子商务集成系统,形成了以ERP为平台支持、连接SCM和CRM的系统结构,并在集成过程中渗透知识管理的思想,以实现对整个供应链的信息集成。依据设计目标,提出了基于电子商务的知识管理体系结构,以有效利用企业知识资源,增强企业的核心竞争力。     

Phase I monitoring of social networks based on Poisson regression profiles

Nowadays, due to the increasing role of social networks in our daily life, monitoring and forecasting social trends have attracted the attention of many researchers. To the best of the authors' knowledge, the literature includes few studies of monitoring social networks. Existing researches have focused on analyzing only the existence of communications between people and have neglected to monitor the number of such communications. In this paper, first counts of communications between people are modeled using Poisson regression profiles. Then, 3 Phase I monitoring methods, extended T², F, and a standardized likelihood ratio test method is suggested to detect step changes, drift, and outliers in the parameters of Poisson regression profiles. The proposed methods are evaluated via simulation studies in terms of signal probability criterion. The results show that in most out‐of‐control situations the standardized likelihood ratio test method outperforms the T² and F methods. Then, a numerical example and a case study based on Enron email data are presented to illustrate the application of the extended methods.     

A supply chain network equilibrium model for operational and opportunism risk mitigation

Risk management holds a crucial role in ensuring efficiency, predictability, and coherency in supply chain operations of an enterprise. Risks are associated with every member of a supply chain network. Thus, an end-to-end risk management approach is essential to fortify the entire supply chain network. In this paper, we consider a supply chain network consisting of suppliers, manufacturers, distributors and retailers, as the representative stakeholders. In particular, we take supply chain operational, and opportunism risks into account, and investigate the roles of flexibility, and social relationship, respectively, as a mitigation approach. We develop a multi-period network equilibrium model by considering the stakeholders’ objectives of maximising profit and minimising risk. Further, the finite-dimensional variational inequality formulations are derived for the underlying network optimisation problem. An algorithm, with nice features for computations, is then applied to three simulated examples in order to illustrate the model and computational procedure as well as the types of interventions that can help the strategic decision-makers to explore quantitatively the associated profits and incurred risks in an entire supply chain network.     

A control chart based approach to monitoring supply network dynamics using Kalman filtering

In today's global market, a critical issue for companies to survive the increasing competition is how to handle uncertainty in their supply network. In this paper, we explore the application of Kalman filtering to estimate the dynamic states in a supply network. Two state-space models are developed. The first one focuses on processing each individual order which includes both waiting time and value-added processing time. Considering the correlation of consecutive orders, the second one enhances the state-space model by employing autoregressive model of waiting time. To signal potential abnormal events, the estimates from the models are further used in control charts with control limits being updated at each monitoring stage according to the related estimation error. A supply network case example is studied and we conclude in the benchmark model (without using Kalman filtering) and the first state-space model, the data collected from the bottleneck stage turns out to be most valuable for increased accuracy in detecting tardy orders. The second state-space model consistently outperforms both the benchmark model and the first state-space model for robustly early detection of abnormalities at all stages, especially the stages before the bottleneck stage, of the system.     

重大建设工程技术创新网络形成机理与运行机制分析

笔者从重大建设工程技术创新的特点和现状分析入手,剖析制约重大建设工程技术创新的核心障碍,建立面向重大建设工程技术创新网络的基本架构,分析其形成起点、演化路径和整合方式,探讨技术创新网络的动力机制、协同机制和学习机制,为建立跨组织协同的新型技术创新模式,提高重大建设工程技术创新效率和水平提供理论依据。     

Reliability analysis of chatter stability for milling process system with uncertainties based on neural network and fourth moment method

A reliability analysis of the milling system with uncertainties is developed in this paper to predict reliable chatter-free machining parameters. The chatter reliability refers to the probability of no chatter for the dynamic milling system. Then a reliability model is established to predict the chatter vibration, in which the dominant modal parameters of the dynamic milling system are defined as random variables. To solve the reliable model with the second-order fourth-moment (SOFM) method, the limiting axial cutting depth is substituted by an explicit expression obtained using a neural network. Therefore, after distributions of the random parameters are experimentally determined, the reliability of the given machining parameters can be computed with the SOFM method. Furthermore, a reliable stability lobe diagram (RSLD) can be plotted to obtain more reliable and accurate stable region instead of the conventional SLD. A case study is performed to validate the feasibility of the proposed method. The reliability of the milling system was calculated with the SOFM method, the first-order second-moment (FOSM) method and the Monte Carlo simulation (MCS) method. The results from the SOFM method and MCS method were found to be more consistent. Moreover, a RSLD with the reliability level 0.99 was compared with a conventional SLD plotted using the mean values of the random parameters. Chatter tests shown that the RSLD with the higher reliability level was more accurate for predicting the chatter-free machining parameters.     

Exponential random graph modeling for diagnosis of out-of-control signals in social network surveillance: Signal interpretation in social networks with ERG modeling

Monitoring of the social networks for detecting anomalous behavior could be vital for the system's survival. This anomalous behavior could raise from any changes in behavior or attributes of a particular individual or groups of individuals in the network and causes structural changes. Multivariate statistical process control charts are effective tools for this purpose while Exponential Random Graph Models are used to model highly interdependent data of the network. So after selecting a model for specific network, T² control charts are used for monitoring the network data to detect any anomalous behavior. Then the Mason, Tracy, and Young method is utilized for interpreting an out-of-control condition. Finally, some real-world examples are used to evaluate the performance of the proposed diagnosis approach. Since complicated dependency in a social network makes different choices in model selection for Exponential Random Graph Models and this causes various results in the evaluation study, if the impact of diagnosis result is not seen in model selection, the appropriate model will not be necessarily selected and this will affect the effectiveness of the whole system. So, in this paper for improving the performance of diagnosis, two indices are introduced and added to model selection criteria and then the appropriate model could be selected based on the decision-maker's preferences.